A few algorithms in RL are also directly inspired by neuroscience and behavioural psychology. Temporal Difference learning (TD), for example, is one such example which has been rigorously studied in the mammalian brain, and the loss function in TD is known to perform in a way the brain spikes, in case of dopamine neurons, when given a reward [1]} [2]} [3]} [4]} [5]}. Schultz et al. [6]} reported that in case of a monkey rewarded with juice, the dopamine level shot up, when the reward was not expected, showing a difference in expected and actual rewards, as in the TD loss function. Overtime, this firing activity back propagated to the earliest reliable stimuli, and once the monkey was fully trained, the firing activity disappeared, and started resulting in a decline on when the expected reward was not produced. The field has not just benefitted unidirectionally, results from TD have also been used in the study of schizophrenia, and the effects of dopamine manipulation on learning [7]}.
